#classification

tsai is a robust open-source deep learning library designed for time series and sequence tasks such as classification, regression, and forecasting. It leverages Pytorch and fastai to integrate innovative models like PatchTST and RNN with Attention. With expanded datasets and Pytorch 2.0 support, tsai offers utilities like walk-forward cross-validation and memory optimization, continually evolving to improve predictive precision. It is well-documented with extensive tutorials, making it a reliable tool for efficient time series data analysis.

Tribuo is a versatile machine learning library designed for Java developers, integrating multi-class classification, regression, and anomaly detection. Developed by Oracle Labs, it ensures smooth deployment with ONNX export and detailed provenance tracking. Fully adaptable via OLCUT and compatible with Java 8+, Tribuo interfaces with prominent tools like LibLinear and TensorFlow, facilitating a unified ML workflow. Engage with the development community through GitHub contributions.

Lazy Predict is an open-source package that simplifies the generation and assessment of machine learning models without intensive coding or parameter adjustments. It allows users to compare various classification and regression models swiftly, determining optimal solutions efficiently. With support for LazyClassifier and LazyRegressor, the tool clarifies model performance metrics including accuracy, F1 score for classifiers, and adjusted R-squared for regressors. Offering comprehensive documentation and easy installation, it serves as a valuable resource for data enthusiasts and professionals for conducting preliminary model evaluations.

TensorFlow Decision Forests (TF-DF) empowers machine learning by supporting the training, execution, and understanding of decision forest models such as Random Forests and Gradient Boosted Trees in TensorFlow. It covers tasks like classification, regression, and ranking, with compatibility with Yggdrasil Decision Forests. Available for Linux and Mac, Windows users can access it via WSL+Linux. TF-DF provides extensive resources, including tutorials, API references, and a developer's manual for guidance on installation, usage, and contributing, making it a strong choice for developers and researchers.